Privacy preserving distributed evaluation framework for embedded personalized systems

ABSTRACT

Systems and processes for evaluating embedded personalized systems are provided. In one example process, instructions that define an experiment associated with a personalized speech recognition system can be received. The instructions can define one or more experimental parameters. In accordance with the received instructions, a second personalized speech recognition system can be generated based on the personalized speech recognition system and the one or more experimental parameters. Additionally, the plurality of user speech samples can be processed using the second personalized speech recognition system to generate a plurality of speech recognition results and a plurality of accuracy scores corresponding to the plurality of speech recognition results. Second instructions can be received based on the plurality of accuracy scores. In accordance with the second instructions, the second speech recognition system can be activated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Ser. No.62/345,401, filed on Jun. 3, 2016, entitled PRIVACY PRESERVINGDISTRIBUTED EVALUATION FRAMEWORK FOR EMBEDDED PERSONALIZED SYSTEMS,which is hereby incorporated by reference in its entirety for allpurposes.

FIELD

This relates generally to evaluating embedded personalized systems onuser devices and, more specifically, to privacy-preserving distributedevaluation frameworks for embedded personalized systems on user devices.

BACKGROUND

Intelligent automated assistants (or digital assistants) can provide abeneficial interface between human users and electronic devices. Suchassistants can allow users to interact with devices or systems usingnatural language in spoken and/or text forms. For example, a user canprovide a speech input containing a user request to a digital assistantoperating on an electronic device. The digital assistant can interpretthe user's intent from the speech input and operationalize the user'sintent into tasks. The tasks can then be performed by executing one ormore services of the electronic device, and a relevant output responsiveto the user request can be returned to the user.

Digital assistants can utilize various statistical systems forprocessing and responding to user requests. For example, digitalassistants can utilize speech recognition systems, machine translationsystems, natural language understanding systems, and speech synthesissystems. The accuracy and robustness of these statistical systems can beenhanced through personalization of the systems. In particular, theunderlying statistical models utilized by the statistical systems can betailored towards a specific user by training the statistical models withuser data. For example, text input received from a user can be used togenerate a personalized language model for a speech recognition system.This can enable the speech recognition system to better recognize uniquewords or phrases (e.g., specific names or locations) that may be lesscommon in general speech, but frequently used by the user.

Personalizing statistical systems with user data can, however, raiseprivacy concerns. For example, users may not want their personalattributes or characteristics reflected in the personalized statisticalmodels to be shared with a third party. One solution for preserving theuser's privacy can be to embed the personalized statistical systems onthe user's device. In particular, the personalized statistical modelscan be generated and stored on the user's device. Further, thepersonalized results obtained from the personalized statistical modelscan remain on the user's device. Third-party access to the user'spersonal data can thus be restricted, which can preserve the user'sprivacy. However, such restricted access can make it difficult toevaluate embedded personalized statistical systems. For example, it canbe difficult to tune the underlying models and algorithms of theembedded personalized statistical systems for optimal performance whenaccess the embedded personalized statistical systems is restricted.

SUMMARY

Systems and processes for evaluating embedded personalized systems areprovided. In one example process, instructions that define an experimentassociated with a personalized speech recognition system can bereceived. The instructions can define one or more experimentalparameters. In accordance with the received instructions, a secondpersonalized speech recognition system can be generated based on thepersonalized speech recognition system and the one or more experimentalparameters. Additionally, the plurality of user speech samples can beprocessed using the second personalized speech recognition system togenerate a plurality of speech recognition results and a plurality ofaccuracy scores corresponding to the plurality of speech recognitionresults. Second instructions can be received based on the plurality ofaccuracy scores. In accordance with the second instructions, the secondspeech recognition system can be activated. User speech input can bereceived. The user speech input can be processed using the activatedsecond personalized speech recognition system to generate a speechrecognition result. A response to the user speech input can be outputtedbased on the speech recognition result.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant according tovarious examples.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the displayaccording to various examples.

FIG. 6A illustrates a personal electronic device according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic deviceaccording to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A according to various examples.

FIG. 7C illustrates a portion of an ontology according to variousexamples.

FIG. 8 is a block diagram illustrating a system for evaluating embeddedpersonalized systems according to various examples.

FIGS. 9A-B illustrate a process for evaluating embedded personalizedsystems according to various examples.

FIG. 10 illustrates a functional block diagram of an electronic deviceaccording to various examples.

DETAILED DESCRIPTION

In the following description of examples, reference is made to theaccompanying drawings in which it is shown by way of illustrationspecific examples that can be practiced. It is to be understood thatother examples can be used, and structural changes can be made, withoutdeparting from the scope of the various examples.

Statistical systems, such as speech recognition systems or naturallanguage understanding systems, can require iterative tuning in order tooptimize performance. For example, each tuning iteration requiresadjusting one or more parameters in the statistical system and thenevaluating the results to determine whether the adjustment improved theperformance of the statistical system. As discussed above, access toembedded personalized statistical systems on user devices is restrictedto preserve the privacy of the user. Such restricted access can, forexample, prevent system developers from evaluating the outcome ofadjustments made to the embedded personalized statistical system. Thismakes it difficult to optimize the performance of embedded personalizestatistical systems.

In accordance with some exemplary systems and processes describedherein, embedded personalized statistical systems on user devices areevaluated and optimized in a privacy preserving manner. In one suchexample process, instructions that define an experiment associated witha personalized speech recognition system are received by a user device.The instructions define one or more experimental parameters. Inaccordance with the received instructions, a second personalized speechrecognition system is generated based on the personalized speechrecognition system and the one or more experimental parameters.Additionally, the plurality of user speech samples are processed usingthe second personalized speech recognition system to generate aplurality of speech recognition results and a plurality of accuracyscores corresponding to the plurality of speech recognition results. Theplurality of accuracy scores are confidence scores indicating thelikelihood of the speech recognition result given the respective userspeech sample. The plurality of accuracy scores are sent to a remoteserver for evaluation. Since the confidence scores are merely likelihoodvalues and contain no personal information, the privacy of the user ispreserved. Based on the plurality of accuracy scores, a system developerdetermines whether the second personalized speech recognition systemshould be activated. For example, if the plurality of accuracy scoresindicate an improvement in the performance of the second personalizedspeech recognition system over the personalized speech recognitionsystem, the system developer sends second instructions to the userdevice to activate the second personalized speech recognition system.The second instructions are received by the user device, and, inaccordance with the second instructions, the second personalized speechrecognition system is activated such that subsequent speech input isprocessed using the second personalized speech recognition system. Byiteratively evaluating experimental parameters in this manner, thepersonalized speech recognition system on the user device is tuned foroptimal performance while still preserving the privacy of the user.

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first speech input could be termed a secondspeech input, and, similarly, a second speech input could be termed afirst speech input, without departing from the scope of the variousdescribed examples. The first speech input and the second speech inputcan both be speech inputs and, in some cases, can be separate anddifferent inputs.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 can implement a digitalassistant. The terms “digital assistant,” “virtual assistant,”“intelligent automated assistant,” or “automatic digital assistant” canrefer to any information processing system that interprets naturallanguage input in spoken and/or textual form to infer user intent, andperforms actions based on the inferred user intent. For example, to acton an inferred user intent, the system can perform one or more of thefollowing: identifying a task flow with steps and parameters designed toaccomplish the inferred user intent, inputting specific requirementsfrom the inferred user intent into the task flow; executing the taskflow by invoking programs, methods, services, APIs, or the like; andgenerating output responses to the user in an audible (e.g., speech)and/or visual form.

Specifically, a digital assistant can be capable of accepting a userrequest at least partially in the form of a natural language command,request, statement, narrative, and/or inquiry. Typically, the userrequest can seek either an informational answer or performance of a taskby the digital assistant. A satisfactory response to the user requestcan be a provision of the requested informational answer, a performanceof the requested task, or a combination of the two. For example, a usercan ask the digital assistant a question, such as “Where am I rightnow?” Based on the user's current location, the digital assistant cananswer, “You are in Central Park near the west gate.” The user can alsorequest the performance of a task, for example, “Please invite myfriends to my girlfriend's birthday party next week.” In response, thedigital assistant can acknowledge the request by saying “Yes, rightaway,” and then send a suitable calendar invite on behalf of the user toeach of the user's friends listed in the user's electronic address book.During performance of a requested task, the digital assistant cansometimes interact with the user in a continuous dialogue involvingmultiple exchanges of information over an extended period of time. Thereare numerous other ways of interacting with a digital assistant torequest information or performance of various tasks. In addition toproviding verbal responses and taking programmed actions, the digitalassistant can also provide responses in other visual or audio forms,e.g., as text, alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant can beimplemented according to a client-server model. The digital assistantcan include client-side portion 102 (hereafter “DA client 102”) executedon user device 104 and server-side portion 106 (hereafter “DA server106”) executed on server system 108. DA client 102 can communicate withDA server 106 through one or more networks 110. DA client 102 canprovide client-side functionalities such as user-facing input and outputprocessing and communication with DA server 106. DA server 106 canprovide server-side functionalities for any number of DA clients 102each residing on a respective user device 104.

In some examples, DA server 106 can include client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112can facilitate the client-facing input and output processing for DAserver 106. One or more processing modules 114 can utilize data andmodels 116 to process speech input and determine the user's intent basedon natural language input. Further, one or more processing modules 114perform task execution based on inferred user intent. In some examples,DA server 106 can communicate with external services 120 throughnetwork(s) 110 for task completion or information acquisition. I/Ointerface to external services 118 can facilitate such communications.

User device 104 can be any suitable electronic device. For example, userdevices can be a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIG. 6A-B.) A portable multifunctional device can be, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices can include the iPhone®, iPod Touch®, and iPad®devices from Apple Inc. of Cupertino, Calif. Other examples of portablemultifunction devices can include, without limitation, laptop or tabletcomputers. Further, in some examples, user device 104 can be anon-portable multifunctional device. In particular, user device 104 canbe a desktop computer, a game console, a television, or a televisionset-top box. In some examples, user device 104 can include atouch-sensitive surface (e.g., touch screen displays and/or touchpads).Further, user device 104 can optionally include one or more otherphysical user-interface devices, such as a physical keyboard, a mouse,and/or a joystick. Various examples of electronic devices, such asmultifunctional devices, are described below in greater detail.

Examples of communication network(s) 110 can include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 can be implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 can be implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 can also employ various virtual devicesand/or services of third-party service providers (e.g., third-partycloud service providers) to provide the underlying computing resourcesand/or infrastructure resources of server system 108.

In some examples, user device 104 can communicate with DA server 106 viasecond user device 122. Second user device 122 can be similar oridentical to user device 104. For example, second user device 122 can besimilar to devices 200, 400, or 600 described below with reference toFIGS. 2A, 4, and 6A-B. User device 104 can be configured tocommunicatively couple to second user device 122 via a directcommunication connection, such as Bluetooth, NFC, BTLE, or the like, orvia a wired or wireless network, such as a local Wi-Fi network. In someexamples, second user device 122 can be configured to act as a proxybetween user device 104 and DA server 106. For example, DA client 102 ofuser device 104 can be configured to transmit information (e.g., a userrequest received at user device 104) to DA server 106 via second userdevice 122. DA server 106 can process the information and returnrelevant data (e.g., data content responsive to the user request) touser device 104 via second user device 122.

In some examples, user device 104 can be configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 can be configured to determine supplemental information toadd to the abbreviated request to generate a complete request totransmit to DA server 106. This system architecture can advantageouslyallow user device 104 having limited communication capabilities and/orlimited battery power (e.g., a watch or a similar compact electronicdevice) to access services provided by DA server 106 by using seconduser device 122, having greater communication capabilities and/orbattery power (e.g., a mobile phone, laptop computer, tablet computer,or the like), as a proxy to DA server 106. While only two user devices104 and 122 are shown in FIG. 1, it should be appreciated that system100 can include any number and type of user devices configured in thisproxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 can include both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant can be implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientcan be a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 may include one or more computer-readable storage mediums.The computer-readable storage mediums may be tangible andnon-transitory. Memory 202 may include high-speed random access memoryand may also include non-volatile memory, such as one or more magneticdisk storage devices, flash memory devices, or other non-volatilesolid-state memory devices. Memory controller 222 may control access tomemory 202 by other components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 can be used to store instructions (e.g., for performingaspects of processes described below) for use by or in connection withan instruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of the processesdescribed below) can be stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or can be dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108. In the context of this document, a “non-transitorycomputer-readable storage medium” can be any medium that can contain orstore the program for use by or in connection with the instructionexecution system, apparatus, or device.

Peripherals interface 218 can be used to couple input and outputperipherals of the device to CPU 220 and memory 202. The one or moreprocessors 220 run or execute various software programs and/or sets ofinstructions stored in memory 202 to perform various functions fordevice 200 and to process data. In some embodiments, peripheralsinterface 218, CPU 220, and memory controller 222 may be implemented ona single chip, such as chip 204. In some other embodiments, they may beimplemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data may be retrievedfrom and/or transmitted to memory 202 and/or RF circuitry 208 byperipherals interface 218. In some embodiments, audio circuitry 210 alsoincludes a headset jack (e.g., 312, FIG. 3). The headset jack providesan interface between audio circuitry 210 and removable audioinput/output peripherals, such as output-only headphones or a headsetwith both output (e.g., a headphone for one or both ears) and input(e.g., a microphone).

In some examples, audio circuitry 210 can include a buffer (e.g.,memory) to store audio data received from peripherals interface 218. Thebuffer can also store audio data converted from the electrical signalsof microphone 213. The buffer can be a circular buffer. The circularbuffer can be a first-in first-out (FIFO) buffer that continuallyoverwrites its contents. The buffer may be of any size, such as forexample 10 or 20 seconds. In some examples, audio circuitry 210 canutilize memory 202 to store audio data.

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button may disengage a lock of touch screen212 or begin a process that uses gestures on the touch screen to unlockthe device, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)may turn power to device 200 on or off. The user may be able tocustomize a functionality of one or more of the buttons. Touch screen212 is used to implement virtual or soft buttons and one or more softkeyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output may includegraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput may correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 may use LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 maydetect contact and any movement or breaking thereof using any of aplurality of touch sensing technologies now known or later developed,including but not limited to capacitive, resistive, infrared, andsurface acoustic wave technologies, as well as other proximity sensorarrays or other elements for determining one or more points of contactwith touch screen 212. In an exemplary embodiment, projected mutualcapacitance sensing technology is used, such as that found in theiPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 may beanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 212 displays visual output from device 200, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 may beas described in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 may have a video resolution in excess of 100 dpi. Insome embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user may make contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200 mayinclude a touchpad (not shown) for activating or deactivating particularfunctions. In some embodiments, the touchpad is a touch-sensitive areaof the device that, unlike the touch screen, does not display visualoutput. The touchpad may be a touch-sensitive surface that is separatefrom touch screen 212 or an extension of the touch-sensitive surfaceformed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 may include a power management system, oneor more power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 may also include one or more optical sensors 264. FIG. 2Ashows an optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 may include charge-coupled device(CCD) or complementary metal-oxide semiconductor (CMOS)phototransistors. Optical sensor 264 receives light from theenvironment, projected through one or more lenses, and converts thelight to data representing an image. In conjunction with imaging module243 (also called a camera module), optical sensor 264 may capture stillimages or video. In some embodiments, an optical sensor is located onthe back of device 200, opposite touch screen display 212 on the frontof the device so that the touch screen display may be used as aviewfinder for still and/or video image acquisition. In someembodiments, an optical sensor is located on the front of the device sothat the user's image may be obtained for video conferencing while theuser views the other video conference participants on the touch screendisplay. In some embodiments, the position of optical sensor 264 can bechanged by the user (e.g., by rotating the lens and the sensor in thedevice housing) so that a single optical sensor 264 may be used alongwith the touch screen display for both video conferencing and stilland/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 may also include one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 may be coupled to input controller 260in I/O subsystem 206. Proximity sensor 266 may perform as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 may also include one or more accelerometers 268. FIG. 2Ashows accelerometer 268 coupled to peripherals interface 218.Alternately, accelerometer 268 may be coupled to an input controller 260in I/O subsystem 206. Accelerometer 268 may perform as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 can store data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views or other information occupy variousregions of touch screen display 212; sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which may be a component of graphics module 232,provides soft keyboards for entering text in various applications (e.g.,contacts 237, e mail 240, IM 241, browser 247, and any other applicationthat needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 can include various client-sidedigital assistant instructions to provide the client-sidefunctionalities of the digital assistant. For example, digital assistantclient module 229 can be capable of accepting voice input (e.g., speechinput), text input, touch input, and/or gestural input through varioususer interfaces (e.g., microphone 213, accelerometer(s) 268,touch-sensitive display system 212, optical sensor(s) 229, other inputcontrol devices 216, etc.) of portable multifunction device 200. Digitalassistant client module 229 can also be capable of providing output inaudio (e.g., speech output), visual, and/or tactile forms throughvarious output interfaces (e.g., speaker 211, touch-sensitive displaysystem 212, tactile output generator(s) 267, etc.) of portablemultifunction device 200. For example, output can be provided as voice,sound, alerts, text messages, menus, graphics, videos, animations,vibrations, and/or combinations of two or more of the above. Duringoperation, digital assistant client module 229 can communicate with DAserver 106 using RF circuitry 208.

User data and models 231 can include various data associated with theuser (e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 can includes various models (e.g., speech recognitionmodels, statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 can utilize thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 can provide the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant can also use thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information can be referred to as context data.

In some examples, the contextual information that accompanies the userinput can include sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also include thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 can be provided to DA server 106 as contextual informationassociated with a user input.

In some examples, the digital assistant client module 229 canselectively provide information (e.g., user data 231) stored on theportable multifunction device 200 in response to requests from DA server106. In some examples, digital assistant client module 229 can alsoelicit additional input from the user via a natural language dialogue orother user interfaces upon request by DA server 106. Digital assistantclient module 229 can pass the additional input to DA server 106 to helpDA server 106 in intent deduction and/or fulfillment of the user'sintent expressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 may include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   E-mail client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which may include one or more of: weather        widget 249-1, stocks widget 249-2, calculator widget 249-3,        alarm clock widget 249-4, dictionary widget 249-5, and other        widgets obtained by the user, as well as user-created widgets        249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that may be stored in memory 202include other word processing applications, other image editingapplications, drawing applications, presentation applications,JAVA-enabled applications, encryption, digital rights management, voicerecognition, and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 may be used to manage an address book orcontact list (e.g., stored in application internal state 292 of contactsmodule 237 in memory 202 or memory 470), including: adding name(s) tothe address book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 may be used to enter a sequence of characters correspondingto a telephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication may use any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, e-mail client module 240 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 244,e-mail client module 240 makes it very easy to create and send e-mailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, the instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages may include graphics, photos, audio files, video filesand/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, e-mail client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that may be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250may be used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254may be used to receive, display, modify, and store maps and dataassociated with maps (e.g., driving directions, data on stores and otherpoints of interest at or near a particular location, and otherlocation-based data) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, e-mail clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than e-mail client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules may be combined or otherwiserearranged in various embodiments. For example, video player module maybe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202may store a subset of the modules and data structures identified above.Furthermore, memory 202 may store additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 may be reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected may correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected may be called the hitview, and the set of events that are recognized as proper inputs may bedetermined based, at least in part, on the hit view of the initial touchthat begins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module, or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 may utilize or call data updater 276,object updater 277, or GUI updater 278 to update the applicationinternal state 292. Alternatively, one or more of the application views291 include one or more respective event handlers 290. Also, in someembodiments, one or more of data updater 276, object updater 277, andGUI updater 278 are included in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283, and event deliveryinstructions 288 (which may include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation may also include speed and direction of the sub-event. Insome embodiments, events include rotation of the device from oneorientation to another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers may interact, or are enabled to interact, with one another.In some embodiments, metadata 283 includes configurable properties,flags, and/or lists that indicate whether sub-events are delivered tovarying levels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 200. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 200 may also include one or more physical buttons, such as “home”or menu button 304. As described previously, menu button 304 may be usedto navigate to any application 236 in a set of applications that may beexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 may be stored in one ormore of the previously mentioned memory devices. Each of theabove-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules may be combined or otherwise rearranged invarious embodiments. In some embodiments, memory 470 may store a subsetof the modules and data structures identified above. Furthermore, memory470 may store additional modules and data structures not describedabove.

Attention is now directed towards embodiments of user interfaces thatmay be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces may be implemented on device400. In some embodiments, user interface 500 includes the followingelements, or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

-   -   Time 504;    -   Bluetooth indicator 505;    -   Battery status indicator 506;    -   Tray 508 with icons for frequently used applications, such as:        -   Icon 516 for telephone module 238, labeled “Phone,” which            optionally includes an indicator 514 of the number of missed            calls or voicemail messages;        -   Icon 518 for e-mail client module 240, labeled “Mail,” which            optionally includes an indicator 510 of the number of unread            e-mails;        -   Icon 520 for browser module 247, labeled “Browser;” and        -   Icon 522 for video and music player module 252, also            referred to as iPod (trademark of Apple Inc.) module 252,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 524 for IM module 241, labeled “Messages;”        -   Icon 526 for calendar module 248, labeled “Calendar;”        -   Icon 528 for image management module 244, labeled “Photos;”        -   Icon 530 for camera module 243, labeled “Camera;”        -   Icon 532 for online video module 255, labeled “Online            Video;”        -   Icon 534 for stocks widget 249-2, labeled “Stocks;”        -   Icon 536 for map module 254, labeled “Maps;”        -   Icon 538 for weather widget 249-1, labeled “Weather;”        -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”        -   Icon 542 for workout support module 242, labeled “Workout            Support;”        -   Icon 544 for notes module 253, labeled “Notes;” and        -   Icon 546 for a settings application or module, labeled            “Settings,” which provides access to settings for device 200            and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 may optionally be labeled “Music” or “Music Player.” Otherlabels are, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 can include some orall of the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4B). In some embodiments, device 600 has touch-sensitivedisplay screen 604, hereafter touch screen 604. Alternatively, or inaddition to touch screen 604, device 600 has a display and atouch-sensitive surface. As with devices 200 and 400, in someembodiments, touch screen 604 (or the touch-sensitive surface) may haveone or more intensity sensors for detecting intensity of contacts (e.g.,touches) being applied. The one or more intensity sensors of touchscreen 604 (or the touch-sensitive surface) can provide output data thatrepresents the intensity of touches. The user interface of device 600can respond to touches based on their intensity, meaning that touches ofdifferent intensities can invoke different user interface operations ondevice 600.

Techniques for detecting and processing touch intensity may be found,for example, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, can be physical.Examples of physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms may permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 can include some or all of the componentsdescribed with respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612that operatively couples I/O section 614 with one or more computerprocessors 616 and memory 618. I/O section 614 can be connected todisplay 604, which can have touch-sensitive component 622 and,optionally, touch-intensity sensitive component 624. In addition, I/Osection 614 can be connected with communication unit 630 for receivingapplication and operating system data, using Wi-Fi, Bluetooth, nearfield communication (NFC), cellular, and/or other wireless communicationtechniques. Device 600 can include input mechanisms 606 and/or 608.Input mechanism 606 may be a rotatable input device or a depressible androtatable input device, for example. Input mechanism 608 may be abutton, in some examples.

Input mechanism 608 may be a microphone, in some examples. Personalelectronic device 600 can include various sensors, such as GPS sensor632, accelerometer 634, directional sensor 640 (e.g., compass),gyroscope 636, motion sensor 638, and/or a combination thereof, all ofwhich can be operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 can be a non-transitorycomputer-readable storage medium, for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, can cause the computer processors to perform thetechniques and processes described below. The computer-executableinstructions can also be stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as a computer-based system, processor-containing system, or othersystem that can fetch the instructions from the instruction executionsystem, apparatus, or device and execute the instructions. For purposesof this document, a “non-transitory computer-readable storage medium”can be any medium that can tangibly contain or store computer-executableinstructions for use by or in connection with the instruction executionsystem, apparatus, or device. The non-transitory computer-readablestorage medium can include, but is not limited to, magnetic, optical,and/or semiconductor storages. Examples of such storage include magneticdisks, optical discs based on CD, DVD, or Blu-ray technologies, as wellas persistent solid-state memory such as flash, solid-state drives, andthe like. Personal electronic device 600 is not limited to thecomponents and configuration of FIG. 6B, but can include other oradditional components in multiple configurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that may be displayed on the displayscreen of devices 200, 400, and/or 600 (FIGS. 2, 4, and 6). For example,an image (e.g., icon), a button, and text (e.g., hyperlink) may eachconstitute an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds mayinclude a first intensity threshold and a second intensity threshold. Inthis example, a contact with a characteristic intensity that does notexceed the first threshold results in a first operation, a contact witha characteristic intensity that exceeds the first intensity thresholdand does not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface may receive a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location may be basedon only a portion of the continuous swipe contact, and not the entireswipe contact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm may be applied tothe intensities of the swipe contact prior to determining thecharacteristic intensity of the contact. For example, the smoothingalgorithm optionally includes one or more of: an unweightedsliding-average smoothing algorithm, a triangular smoothing algorithm, amedian filter smoothing algorithm, and/or an exponential smoothingalgorithm. In some circumstances, these smoothing algorithms eliminatenarrow spikes or dips in the intensities of the swipe contact forpurposes of determining a characteristic intensity.

The intensity of a contact on the touch-sensitive surface may becharacterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs sometimes termed “jitter,” where the device defines orselects a hysteresis intensity threshold with a predefined relationshipto the press-input intensity threshold (e.g., the hysteresis intensitythreshold is X intensity units lower than the press-input intensitythreshold or the hysteresis intensity threshold is 75%, 90%, or somereasonable proportion of the press-input intensity threshold). Thus, insome embodiments, the press input includes an increase in intensity ofthe respective contact above the press-input intensity threshold and asubsequent decrease in intensity of the contact below the hysteresisintensity threshold that corresponds to the press-input intensitythreshold, and the respective operation is performed in response todetecting the subsequent decrease in intensity of the respective contactbelow the hysteresis intensity threshold (e.g., an “up stroke” of therespective press input). Similarly, in some embodiments, the press inputis detected only when the device detects an increase in intensity of thecontact from an intensity at or below the hysteresis intensity thresholdto an intensity at or above the press-input intensity threshold and,optionally, a subsequent decrease in intensity of the contact to anintensity at or below the hysteresis intensity, and the respectiveoperation is performed in response to detecting the press input (e.g.,the increase in intensity of the contact or the decrease in intensity ofthe contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 can be implemented on a standalone computer system. In someexamples, digital assistant system 700 can be distributed acrossmultiple computers. In some examples, some of the modules and functionsof the digital assistant can be divided into a server portion and aclient portion, where the client portion resides on one or more userdevices (e.g., devices 104, 122, 200, 400, or 600) and communicates withthe server portion (e.g., server system 108) through one or morenetworks, e.g., as shown in FIG. 1. In some examples, digital assistantsystem 700 can be an implementation of server system 108 (and/or DAserver 106) shown in FIG. 1. It should be noted that digital assistantsystem 700 is only one example of a digital assistant system, and thatdigital assistant system 700 can have more or fewer components thanshown, may combine two or more components, or may have a differentconfiguration or arrangement of the components. The various componentsshown in FIG. 7A can be implemented in hardware, software instructionsfor execution by one or more processors, firmware, including one or moresignal processing and/or application specific integrated circuits, or acombination thereof.

Digital assistant system 700 can include memory 702, one or moreprocessors 704, input/output (I/O) interface 706, and networkcommunications interface 708. These components can communicate with oneanother over one or more communication buses or signal lines 710.

In some examples, memory 702 can include a non-transitorycomputer-readable medium, such as high-speed random access memory and/ora non-volatile computer-readable storage medium (e.g., one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices).

In some examples, I/O interface 706 can couple input/output devices 716of digital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, can receive userinputs (e.g., voice input, keyboard inputs, touch inputs, etc.) andprocesses them accordingly. In some examples, e.g., when the digitalassistant is implemented on a standalone user device, digital assistantsystem 700 can include any of the components and I/O communicationinterfaces described with respect to devices 200, 400, or 600 in FIGS.2A, 4, 6A-B, respectively. In some examples, digital assistant system700 can represent the server portion of a digital assistantimplementation, and can interact with the user through a client-sideportion residing on a user device (e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 can includewired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) can receive andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 can receive and send RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications can use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 can enable communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, can store programs, modules, instructions, and datastructures including all or a subset of: operating system 718,communications module 720, user interface module 722, one or moreapplications 724, and digital assistant module 726. In particular,memory 702, or the computer-readable storage media of memory 702, canstore instructions for performing the processes described below. One ormore processors 704 can execute these programs, modules, andinstructions, and reads/writes from/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) can includevarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 can facilitate communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 can communicatewith RF circuitry 208 of electronic devices such as devices 200, 400,and 600 shown in FIG. 2A, 4, 6A-B, respectively. Communications module720 can also include various components for handling data received bywireless circuitry 714 and/or wired communications port 712.

User interface module 722 can receive commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 can also prepare anddeliver outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 can include programs and/or modules that are configuredto be executed by one or more processors 704. For example, if thedigital assistant system is implemented on a standalone user device,applications 724 can include user applications, such as games, acalendar application, a navigation application, or an email application.If digital assistant system 700 is implemented on a server, applications724 can include resource management applications, diagnosticapplications, or scheduling applications, for example.

Memory 702 can also store digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 can include the following sub-modules, or a subset orsuperset thereof: input/output processing module 728, speech-to-text(STT) processing module 730, natural language processing module 732,dialogue flow processing module 734, task flow processing module 736,service processing module 738, and speech synthesis module 740. Each ofthese modules can have access to one or more of the following systems ordata and models of the digital assistant module 726, or a subset orsuperset thereof: ontology 760, vocabulary index 744, user data 748,task flow models 754, service models 756, and ASR systems.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728 caninteract with the user through I/O devices 716 in FIG. 7A or with a userdevice (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 can optionally obtain contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation can include user-specific data, vocabulary, and/orpreferences relevant to the user input. In some examples, the contextualinformation also includes software and hardware states of the userdevice at the time the user request is received, and/or informationrelated to the surrounding environment of the user at the time that theuser request was received. In some examples, I/O processing module 728can also send follow-up questions to, and receive answers from, the userregarding the user request. When a user request is received by I/Oprocessing module 728 and the user request can include speech input, I/Oprocessing module 728 can forward the speech input to STT processingmodule 730 (or speech recognizer) for speech-to-text conversions.

STT processing module 730 can include one or more ASR systems. The oneor more ASR systems can process the speech input that is receivedthrough I/O processing module 728 to produce a recognition result. EachASR system can include a front-end speech pre-processor. The front-endspeech pre-processor can extract representative features from the speechinput. For example, the front-end speech pre-processor can perform aFourier transform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system can include one ormore speech recognition models (e.g., acoustic models and/or languagemodels) and can implement one or more speech recognition engines.Examples of speech recognition models can include Hidden Markov Models,Gaussian-Mixture Models, Deep Neural Network Models, n-gram languagemodels, and other statistical models. Examples of speech recognitionengines can include the dynamic time warping based engines and weightedfinite-state transducers (WFST) based engines. The one or more speechrecognition models and the one or more speech recognition engines can beused to process the extracted representative features of the front-endspeech pre-processor to produce intermediate recognitions results (e.g.,phonemes, phonemic strings, and sub-words), and ultimately, textrecognition results (e.g., words, word strings, or sequence of tokens).In some examples, the speech input can be processed at least partiallyby a third-party service or on the user's device (e.g., device 104, 200,400, or 600) to produce the recognition result. Once STT processingmodule 730 produces recognition results containing a text string (e.g.,words, or sequence of words, or sequence of tokens), the recognitionresult can be passed to natural language processing module 732 forintent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 can include and/or access avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word can be associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words caninclude a word that is associated with a plurality of candidatepronunciations. For example, the vocabulary may include the word“tomato” that is associated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words can be associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations can be stored in STT processingmodule 730 and can be associated with a particular user via the user'sprofile on the device. In some examples, the candidate pronunciationsfor words can be determined based on the spelling of the word and one ormore linguistic and/or phonetic rules. In some examples, the candidatepronunciations can be manually generated, e.g., based on known canonicalpronunciations.

In some examples, the candidate pronunciations can be ranked based onthe commonness of the candidate pronunciation. For example, thecandidate pronunciation /

/can be ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations can be ranked based on whether the candidatepronunciation is a custom candidate pronunciation associated with theuser. For example, custom candidate pronunciations can be ranked higherthan canonical candidate pronunciations. This can be useful forrecognizing proper nouns having a unique pronunciation that deviatesfrom canonical pronunciation. In some examples, candidate pronunciationscan be associated with one or more speech characteristics, such asgeographic origin, nationality, or ethnicity. For example, the candidatepronunciation /

/ can be associated with the United States, whereas the candidatepronunciation /

/ can be associated with Great Britain. Further, the rank of thecandidate pronunciation can be based on one or more characteristics(e.g., geographic origin, nationality, ethnicity, etc.) of the userstored in the user's profile on the device. For example, it can bedetermined from the user's profile that the user is associated with theUnited States. Based on the user being associated with the UnitedStates, the candidate pronunciation /

/ (associated with the United States) can be ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations can be selected as a predicted pronunciation(e.g., the most likely pronunciation).

When a speech input is received, STT processing module 730 can be usedto determine the phonemes corresponding to the speech input (e.g., usingan acoustic model), and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 can first identify the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 can use approximate matchingtechniques to determine words in an utterance. Thus, for example, theSTT processing module 730 can determine that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant can take the sequence of words or tokens (“tokensequence”) generated by STT processing module 730, and attempt toassociate the token sequence with one or more “actionable intents”recognized by the digital assistant. An “actionable intent” canrepresent a task that can be performed by the digital assistant, and canhave an associated task flow implemented in task flow models 754. Theassociated task flow can be a series of programmed actions and stepsthat the digital assistant takes in order to perform the task. The scopeof a digital assistant's capabilities can be dependent on the number andvariety of task flows that have been implemented and stored in task flowmodels 754, or in other words, on the number and variety of “actionableintents” that the digital assistant recognizes. The effectiveness of thedigital assistant, however, can also be dependent on the assistant'sability to infer the correct “actionable intent(s)” from the userrequest expressed in natural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 can also receive contextual information associated with theuser request, e.g., from I/O processing module 728. The natural languageprocessing module 732 can optionally use the contextual information toclarify, supplement, and/or further define the information contained inthe token sequence received from STT processing module 730. Thecontextual information can include, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information can bedynamic, and can change with time, location, content of the dialogue,and other factors.

In some examples, the natural language processing can be based on, e.g.,ontology 760. Ontology 760 can be a hierarchical structure containingmany nodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” can represent atask that the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” can represent a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 can define how a parameter represented by theproperty node pertains to the task represented by the actionable intentnode.

In some examples, ontology 760 can be made up of actionable intent nodesand property nodes. Within ontology 760, each actionable intent node canbe linked to one or more property nodes either directly or through oneor more intermediate property nodes. Similarly, each property node canbe linked to one or more actionable intent nodes either directly orthrough one or more intermediate property nodes. For example, as shownin FIG. 7C, ontology 760 can include a “restaurant reservation” node(i.e., an actionable intent node). Property nodes “restaurant,”“date/time” (for the reservation), and “party size” can each be directlylinked to the actionable intent node (i.e., the “restaurant reservation”node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” can be sub-nodes of the property node “restaurant,” andcan each be linked to the “restaurant reservation” node (i.e., theactionable intent node) through the intermediate property node“restaurant.” For another example, as shown in FIG. 7C, ontology 760 canalso include a “set reminder” node (i.e., another actionable intentnode). Property nodes “date/time” (for setting the reminder) and“subject” (for the reminder) can each be linked to the “set reminder”node. Since the property “date/time” can be relevant to both the task ofmaking a restaurant reservation and the task of setting a reminder, theproperty node “date/time” can be linked to both the “restaurantreservation” node and the “set reminder” node in ontology 760.

An actionable intent node, along with its linked concept nodes, can bedescribed as a “domain.” In the present discussion, each domain can beassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Ccan include an example of restaurant reservation domain 762 and anexample of reminder domain 764 within ontology 760. The restaurantreservation domain includes the actionable intent node “restaurantreservation,” property nodes “restaurant,” “date/time,” and “partysize,” and sub-property nodes “cuisine,” “price range,” “phone number,”and “location.” Reminder domain 764 can include the actionable intentnode “set reminder,” and property nodes “subject” and “date/time.” Insome examples, ontology 760 can be made up of many domains. Each domaincan share one or more property nodes with one or more other domains. Forexample, the “date/time” property node can be associated with manydifferent domains (e.g., a scheduling domain, a travel reservationdomain, a movie ticket domain, etc.), in addition to restaurantreservation domain 762 and reminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains can include, for example, “find a movie,” “initiate a phonecall,” “find directions,” “schedule a meeting,” “send a message,” and“provide an answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task” and so on. A “send amessage” domain can be associated with a “send a message” actionableintent node, and may further include property nodes such as“recipient(s),” “message type,” and “message body.” The property node“recipient” can be further defined, for example, by the sub-propertynodes such as “recipient name” and “message address.”

In some examples, ontology 760 can include all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 can bemodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents can be clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain can include a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel can include “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) can have many property nodes incommon. For example, the actionable intent nodes for “airlinereservation,” “hotel reservation,” “car rental,” “get directions,” and“find points of interest” can share one or more of the property nodes“start location,” “destination,” “departure date/time,” “arrivaldate/time,” and “party size.”

In some examples, each node in ontology 760 can be associated with a setof words and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node can be the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node can be stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” can include words such as“food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,”“meal,” and so on. For another example, the vocabulary associated withthe node for the actionable intent of “initiate a phone call” caninclude words and phrases such as “call,” “phone,” “dial,” “ring,” “callthis number,” “make a call to,” and so on. The vocabulary index 744 canoptionally include words and phrases in different languages.

Natural language processing module 732 can receive the token sequence(e.g., a text string) from STT processing module 730, and determine whatnodes are implicated by the words in the token sequence. In someexamples, if a word or phrase in the token sequence is found to beassociated with one or more nodes in ontology 760 (via vocabulary index744), the word or phrase can “trigger” or “activate” those nodes. Basedon the quantity and/or relative importance of the activated nodes,natural language processing module 732 can select one of the actionableintents as the task that the user intended the digital assistant toperform. In some examples, the domain that has the most “triggered”nodes can be selected. In some examples, the domain having the highestconfidence value (e.g., based on the relative importance of its varioustriggered nodes) can be selected. In some examples, the domain can beselected based on a combination of the number and the importance of thetriggered nodes. In some examples, additional factors are considered inselecting the node as well, such as whether the digital assistant haspreviously correctly interpreted a similar request from a user.

User data 748 can include user-specific information, such asuser-specific vocabulary, user preferences, user address, user's defaultand secondary languages, user's contact list, and other short-term orlong-term information for each user. In some examples, natural languageprocessing module 732 can use the user-specific information tosupplement the information contained in the user input to further definethe user intent. For example, for a user request “invite my friends tomy birthday party,” natural language processing module 732 can be ableto access user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

Other details of searching an ontology based on a token string isdescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 can generate a structured query torepresent the identified actionable intent. In some examples, thestructured query can include parameters for one or more nodes within thedomain for the actionable intent, and at least some of the parametersare populated with the specific information and requirements specifiedin the user request. For example, the user may say “Make me a dinnerreservation at a sushi place at 7.” In this case, natural languageprocessing module 732 can be able to correctly identify the actionableintent to be “restaurant reservation” based on the user input. Accordingto the ontology, a structured query for a “restaurant reservation”domain may include parameters such as {Cuisine}, {Time}, {Date}, {PartySize}, and the like. In some examples, based on the speech input and thetext derived from the speech input using STT processing module 730,natural language processing module 732 can generate a partial structuredquery for the restaurant reservation domain, where the partialstructured query includes the parameters {Cuisine=“Sushi”} and {Time=“7pm”}. However, in this example, the user's utterance containsinsufficient information to complete the structured query associatedwith the domain. Therefore, other necessary parameters such as {PartySize} and {Date} may not be specified in the structured query based onthe information currently available. In some examples, natural languageprocessing module 732 can populate some parameters of the structuredquery with received contextual information. For example, in someexamples, if the user requested a sushi restaurant “near me,” naturallanguage processing module 732 can populate a {location} parameter inthe structured query with GPS coordinates from the user device.

In some examples, natural language processing module 732 can pass thegenerated structured query (including any completed parameters) to taskflow processing module 736 (“task flow processor”). Task flow processingmodule 736 can be configured to receive the structured query fromnatural language processing module 732, complete the structured query,if necessary, and perform the actions required to “complete” the user'sultimate request. In some examples, the various procedures necessary tocomplete these tasks can be provided in task flow models 754. In someexamples, task flow models 754 can include procedures for obtainingadditional information from the user and task flows for performingactions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 may need to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,task flow processing module 736 can invoke dialogue flow processingmodule 734 to engage in a dialogue with the user. In some examples,dialogue flow processing module 734 can determine how (and/or when) toask the user for the additional information and receives and processesthe user responses. The questions can be provided to and answers can bereceived from the users through I/O processing module 728. In someexamples, dialogue flow processing module 734 can present dialogueoutput to the user via audio and/or visual output, and receives inputfrom the user via spoken or physical (e.g., clicking) responses.Continuing with the example above, when task flow processing module 736invokes dialogue flow processing module 734 to determine the “partysize” and “date” information for the structured query associated withthe domain “restaurant reservation,” dialogue flow processing module 734can generate questions such as “For how many people?” and “On whichday?” to pass to the user. Once answers are received from the user,dialogue flow processing module 734 can then populate the structuredquery with the missing information, or pass the information to task flowprocessing module 736 to complete the missing information from thestructured query.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 can proceed toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 can execute the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” can include steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flowprocessing module 736 can perform the steps of: (1) logging onto aserver of the ABC Café or a restaurant reservation system such asOPENTABLE®, (2) entering the date, time, and party size information in aform on the website, (3) submitting the form, and (4) making a calendarentry for the reservation in the user's calendar.

In some examples, task flow processing module 736 can employ theassistance of service processing module 738 (“service processingmodule”) to complete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 can act on behalf of task flow processing module736 to make a phone call, set a calendar entry, invoke a map search,invoke or interact with other user applications installed on the userdevice, and invoke or interact with third-party services (e.g., arestaurant reservation portal, a social networking website, a bankingportal, etc.). In some examples, the protocols and applicationprogramming interfaces (API) required by each service can be specifiedby a respective service model among service models 756. Serviceprocessing module 738 can access the appropriate service model for aservice and generate requests for the service in accordance with theprotocols and APIs required by the service according to the servicemodel.

For example, if a restaurant has enabled an online reservation service,the restaurant can submit a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 can establish a network connection with the onlinereservation service using the web address stored in the service model,and send the necessary parameters of the reservation (e.g., time, date,party size) to the online reservation interface in a format according tothe API of the online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 can be usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse can be a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response can be output as a speech output. In these examples,the generated response can be sent to speech synthesis module 740 (e.g.,speech synthesizer) where it can be processed to synthesize the dialogueresponse in speech form. In yet other examples, the generated responsecan be data content relevant to satisfying a user request in the speechinput.

Speech synthesis module 740 can be configured to synthesize speechoutputs for presentation to the user. Speech synthesis module 740synthesizes speech outputs based on text provided by the digitalassistant. For example, the generated dialogue response can be in theform of a text string. Speech synthesis module 740 can convert the textstring to an audible speech output. Speech synthesis module 740 can useany appropriate speech synthesis technique in order to generate speechoutputs from text, including, but not limited, to concatenativesynthesis, unit selection synthesis, diphone synthesis, domain-specificsynthesis, formant synthesis, articulatory synthesis, hidden Markovmodel (HMM) based synthesis, and sinewave synthesis. In some examples,speech synthesis module 740 can be configured to synthesize individualwords based on phonemic strings corresponding to the words. For example,a phonemic string can be associated with a word in the generateddialogue response. The phonemic string can be stored in metadataassociated with the word. Speech synthesis model 740 can be configuredto directly process the phonemic string in the metadata to synthesizethe word in speech form.

In some examples, instead of (or in addition to) using speech synthesismodule 740, speech synthesis can be performed on a remote device (e.g.,the server system 108), and the synthesized speech can be sent to theuser device for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it can be possible to obtainhigher quality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

FIG. 8 illustrates system 800 for evaluating embedded personalizedsystems according to various examples. As shown, system 800 can includedistributed evaluation server 806 communicatively coupled to algorithmgenerating device 808 via I/O interface 818. Further, distributedevaluation server 806 can be communicatively coupled to user devices820-826 via network(s) 810. For simplicity, system 800 is depicted ashaving only four user devices 820-826. It should be recognized, though,that system 800 can include any number of user devices 820-826. Forexample, distributed evaluation server 806 can be communicativelycoupled to a large population of user devices (e.g., greater than 10,000user devices) in order to evaluate experimental parameters at scale.

Algorithm generating device 808 can be an electronic device (e.g.,similar to device 400) implementing one or more programs for evaluatingstatistical systems. In some examples, algorithm generating device 808can be configured to create experimental definitions, to be distributedby distributed evaluation server 806 to participating user devices820-826. In one implementation, the experimental definitions can defineone or more experimental parameters. The one or more experimentalparameters can include personalization parameters that change thebehavior of an embedded statistical systems. For example, apersonalization parameter can instruct the embedded statistical systemto incorporate user-dependent language statistics with a predeterminedweighting factor (e.g., 0.3). The user-dependent language statistic canbe, for example, derived from user e-mails received at the user device.In other examples, personalization parameters can instruct the embeddedstatistical system to evaluate its accuracy in a predetermined manner,such as by selecting from one of several evaluation schemes available onthe user device. In another implementation, the experimental definitioncan include one or more programs, implementing novel personalizationalgorithms and/or novel evaluation schemes. In yet anotherimplementation, the experimental definition can include completestatistical systems or system components (e.g. models, decoders).

A system developer can interact with algorithm generating device 808 todesign an experiment for optimizing embedded personalized systems on apopulation of user devices (e.g., user devices 820-826). In particular,the experiment can define one or more experimental parameters to beevaluated. Algorithm generating device 808 can send the experiment todistributed evaluation server 806 for the experiment to be distributedto user devices 820-826. Although FIG. 8 depicts algorithm generatingdevice 808 as being directly coupled to distributed evaluation server806 via I/O interface 818, it should be appreciated that, in someexamples, algorithm generating device 808 can be communicatively coupledto distributed evaluation server 806 via network(s) 810. Network(s) 810can be similar or identical to network(s) 110, described above.

Distributed evaluation server 806 can include processing modules 814,data & models 816, and I/O interface 818. In some examples, distributedevaluation server 806 can be part of server system 108 (FIG. 1) and canexchange data and information with DA server 106. Processing modules814, data & models 816, and I/O interface 818 can be similar toprocessing modules 114, data & models 116, and I/O interface to DAclient 112, respectively. Additionally, processing module 814 caninclude distributed evaluation module 815 that is operable to performdistributed evaluation processes (e.g., process 900) for embeddedpersonalized systems. For example, distributed evaluation server 806 caninstruct a population of user devices to store user data samples (e.g.,user speech samples) for evaluating embedded personalized systems.Distributed evaluation server 806 can also receive the experiment fromalgorithm generating device 808 and generate corresponding instructions(e.g., the instructions of block 902) for performing the experiment.Additionally, distributed evaluation server 806 can distribute theinstructions to user devices 820-826 and cause each of user devices820-826 to execute the instructions.

User devices 820-826 can each be electronic devices similar to userdevices 104, 200, or 400. In some examples, each of user devices 820-826can implement at least a portion of digital assistant system 700. Forexample, each of user devices 820-826 can include one or more embeddedpersonalized systems (e.g., ASR system(s) 731, natural languageprocessing system, or speech synthesis system) of the digital assistantsystem 700. Further, each of user devices 820-826 can includeclient-side distributed evaluation module 817 for receiving andexecuting the instructions of distributed evaluation module 815. Inparticular, client-side distributed evaluation module 817 can executethe instructions to cause each of user devices 820-826 to generate asecond embedded personalized system from an existing embeddedpersonalized system. The second embedded personalized system can begenerated by incorporating the one or more experimental parameters intothe existing embedded personalized system. Client-side distributedevaluation module 817 can generate evaluation data (e.g., accuracyscores) for evaluating the one or more experimental parameters. Inparticular, the user data samples can be processed through the secondembedded personalized system to generate the evaluation data.Client-side distributed evaluation module 817 can then transmit theevaluation data back to distributed evaluation server 806 for analysis.

In some examples, distributed evaluation module 815 can be configured toanalyze the evaluation data from each of user devices 820-826 todetermine whether the one or more experimental parameters causedimprovements in the performance of the existing embedded personalizedsystem. In response to determining that the one or more experimentalparameters caused improvements in the performance of the existingembedded personalized system, distributed evaluation module 815 cangenerate second instructions for activating the second embeddedpersonalized system in each of user devices 820-826.

In other examples, distributed evaluation module 815 can forward theevaluation data to algorithm generating device 808 for the systemdeveloper to analyze. If the system developer determines from theevaluation data that the one or more experimental parameters causedimprovements in the performance of the existing embedded personalizedsystem, the system developers can provide input to distributedevaluation module 815 via algorithm generating device 808 to generatesecond instructions for activating the second embedded personalizedsystem in each of user devices 820-826.

Distributed evaluation module 815 can distribute the second instructionsto each of devices 820-826. Client-side distributed evaluation module817 can execute the second instructions and cause the second embeddedpersonalized system to be activated in each of user devices 820-826.Activating the second embedded personalized system can cause the secondembedded personalized system to be used in place of the existingembedded personalized system. In this manner, the embedded personalizedsystem of each user device can be optimized using system 800.

4. Processes for Evaluating Embedded Personalized Systems

FIGS. 9A-B illustrate process 900 for evaluating embedded personalizedsystems according to various examples. Process 900 can be performedusing one or more electronic devices implementing a digital assistant.In some examples, process 900 can be performed at a client-server system(e.g., systems 100 and/or 800). In some examples, process 900 can beperformed at an electronic device (e.g., device 104, 200, 400, or 600).In process 900, some blocks are, optionally, combined, the order of someblocks are, optionally, changed, and some blocks are, optionally,omitted.

One or more statistical systems can be stored in the memory (e.g.,memory 202, or 470) of the electronic device. For example, a speechrecognition system (e.g., ASR system(s) 731) can be stored in the memoryof the electronic device. As discussed above, the speech recognitionsystem can include one or more speech recognition engines (e.g.,algorithms) and one or more speech recognition models (e.g., acousticand/or language models). Further, the one or more statistical systemscan be personalized (e.g., user-dependent) based on a user of theelectronic device. For example, the stored speech recognition system canbe a personalized speech recognition system that uses one or morepersonalized speech recognition models. The one or more personalizedspeech recognition models can be trained using data associated with theuser of the electronic device (e.g., user data). In one example, the oneor more personalized speech recognition models can include apersonalized language model generated from text associated with theuser. The text associated with the user can include text input from theuser, transcribed speech input from the user, user-specific text data(e.g., contacts, music metadata, maps data, etc.) stored on theelectronic device, or messages (e.g., text messages, emails, etc.) inwhich the user is the recipient. In another example, the one or morepersonalized speech recognition models can include a personalizedacoustic model generated from speech input received at the electronicdevice from the user. In some examples, the one or more personalizedspeech recognition models can be generated on the user device (e.g., oneof user devices 820-826).

The memory of the electronic device can further store a plurality ofuser speech samples. The plurality of user speech samples can be derivedfrom a plurality of speech inputs that are associated with a user of theelectronic device. Specifically, the plurality of speech inputs can bereceived at the electronic device from the user. For example, the usercan periodically provide spoken user requests while interacting with thedigital assistant implemented on the electronic device. A predeterminedportion (e.g., one percent) of the spoken user requests can be randomlycaptured and stored as user speech samples. In some examples, the spokenuser requests can be sampled in a privacy preserving manner.Specifically, the spoken user request can be sampled randomly and thesize of each sampled spoken user request can be limited in size and/orlength. Additionally, the total duration of user speech sampled can belimited (e.g., at most 10 utterances or less than 1 minute of cumulativeuser speech). Further, in some examples, the user speech samples can bestored on a rolling basis, where each user speech sample is discardedafter a predetermined amount of time and replaced with a new user speechsample. This can limit the amount of meaningful personal informationcaptured at any given time. In some examples, the plurality of speechinputs can be received and the plurality of user speech samples can bestored prior to receiving the instructions at block 902. In otherexamples, the plurality of speech inputs can be received and theplurality of user speech samples can be stored prior to processing theplurality of user speech samples using the second personalized speechrecognition system at block 906.

At block 902, instructions that define an experiment can be received(e.g., at client-side distributed evaluation module 817 and via RFcircuitry 208 or network communications interface 460, 708). Theexperiment can be associated with a personalized speech recognitionsystem stored in the memory of the electronic device. The instructionscan be received from a server (e.g., distributed evaluation server 806).For example, the server can push the instructions onto a plurality ofuser devices (e.g., user devices 820-826), which include the electronicdevice. Alternatively, the electronic device can be configured toretrieve the instructions from the server. The received instructions candefine one or more experimental parameters to be evaluated using thepersonalized speech recognition system. For example, the instructionscan include algorithms for evaluating the one or more experimentalparameters. The algorithms can be designed by a system developer (e.g.,at algorithm generating device 808). The received instructions, whenexecuted by the electronic device, can cause the electronic device togenerate experimental data (e.g., as described in blocks 904 and 906)that can be used to evaluate the one or more experimental parameters.

In some examples, the one or more experimental parameters can includeone or more experimental interpolation weighting parameters forinterpolating between a general (e.g., user-independent) speechrecognition model and a personalized (e.g., user-dependent) speechrecognition model of the personalized speech recognition system. The oneor more experimental interpolation weighting parameters can be differentfrom the one or more default interpolation weighting parametersimplemented in the personalized speech recognition system. In otherexamples, the one or more experimental parameters can include one ormore machine learning hyperparameters. Hyperparameters can, for example,adjust the capacity of the speech recognition system, such as itsflexibility and the degrees of freedom available for fitting speechdata.

At block 904, a second personalized speech recognition system can begenerated (e.g., by client-side distributed evaluation module 817) inaccordance with the instructions of block 902. The second personalizedspeech recognition system can be generated based on the personalizedspeech recognition system and the one or more experimental parameters.In some examples, the second personalized speech recognition system canbe generated by substituting one or more relevant parameters of thepersonalized speech recognition system with the one or more experimentalparameters. In other examples, the second personalized speechrecognition system can be generated by installing one or more secondpersonalized speech recognition engines (e.g., algorithms) thatincorporate the one or more experimental parameters. The one or moresecond personalized speech recognition engines can be utilized by thesecond personalized speech recognition system. In yet other examples,the second personalized speech recognition system can be generated bytraining one or more second speech recognition models using the one ormore experimental parameters. The one or more second speech recognitionmodels can be utilized by the second personalized speech recognitionsystem. The second personalized speech recognition system can remaininactive on the electronic device prior to receiving the secondinstructions at block 912. Specifically, user input (e.g., speech input)received at the electronic device may not be processed by the secondpersonalized speech recognition system prior to the second personalizedspeech recognition system being activated.

At block 906, in accordance with the instructions received at block 902,the plurality of user speech samples stored on the electronic device canbe processed (e.g., by client-side distributed evaluation module 817and/or STT processing module 730) using the second personalized speechrecognition system. Processing the plurality of user speech samplesusing the second personalized speech recognition system can generate aplurality of speech recognition results. The plurality of speechrecognition results can be text representations that correspond to theplurality of user speech samples. Additionally, a plurality of accuracyscores corresponding to the plurality of speech recognition results canbe generated by processing the plurality of user speech samples usingthe second personalized speech recognition system. Specifically, theplurality of accuracy scores can be generated from one or more speechrecognition models used by the second speech recognition system.

In some examples, the plurality of accuracy scores are not actual errorrates. For example, each accuracy score can be a confidence scorerepresenting a likelihood of the generated speech recognition resultgiven the respective user speech sample. In some examples, the pluralityof accuracy scores can be estimated error rates (e.g., estimated worderror rate). In a specific example, each accuracy score can representthe estimated number of incorrect words given one hundred wordsgenerated using the second personalized speech recognition system. Thus,in some examples, the plurality of accuracy scores can be confidencescores generated without comparing the plurality of speech recognitionresults to a plurality of reference text. For example, the plurality ofaccuracy scores may not be actual word error rates generated bycomparing the plurality of speech recognition results to verifiedreference text.

In examples where the plurality of accuracy scores are generated withoutcomparing the plurality of speech recognition results to a plurality ofreference text, the plurality of speech recognition results can remainon the electronic device. In particular, the plurality of speechrecognition results may not be transmitted to any remote electronicdevice. In this way, user specific data (e.g., the speech recognitionresults generated using personalized speech recognition models) wouldnot be sent to a third-party device (e.g., distributed evaluation server806), thereby preserving the privacy of the user.

In some examples, the plurality of speech recognition results can bebased on one or more speech recognition models of the personalizedspeech recognition system and one or more second speech recognitionmodels generated based on the one or more experimental parameters. Inthese examples, the plurality of accuracy scores can be derived from theone or more speech recognition models of the personalized speechrecognition system. For example, the personalized speech recognitionsystem can include one or more acoustic models and one or more languagemodels. The instructions of block 902 can cause one or more experimentallanguage models to be generated using the one or more experimentalparameters. The second personalized speech recognition system canutilize the one or more acoustic models of the personalized speechrecognition system and the one or more experimental language models togenerate the plurality of speech recognition results. In this example,because the one or more experimental language models are different fromthe one or more language models of the personalized speech recognitionsystem, accuracy scores (e.g., confidence scores) generated by the oneor more experimental language models cannot be meaningfully comparedagainst accuracy scores generated by the one or more language models ofthe personalized speech recognition system. In order to meaningfullycompare the second personalized speech recognition system against thepersonalized speech recognition system, accuracy scores can be generatedusing only the one or more acoustic models that are utilized by both thepersonalized speech recognition system and the second personalizedspeech recognition system. Thus, in this example, the plurality ofaccuracy scores can be generated using the one or more acoustic modelsof the personalized speech recognition system. Specifically, theplurality of accuracy scores may not be derived from the one or moreexperimental language models. Similarly, in an alternative example, oneor more experimental acoustic models can be generated using the one ormore experimental parameters. The second personalized speech recognitionsystem can utilize the one or more experimental acoustic models and oneor more language models of the personalized speech recognition system togenerate the plurality of speech recognition results. In this example,the plurality of accuracy scores can be generated using the one or morelanguage models of the personalized speech recognition system and notderived from the one or more experimental acoustic models.

In other examples, the plurality of accuracy scores can be generated bycomparing the plurality of speech recognition results to a plurality ofreference text. In these examples, the plurality of accuracy scores canrepresent actual error rates (e.g., actual word error rates). Thecomparing can be performed on the electronic device to preserve theprivacy of the user. In particular, the plurality of speech recognitionresults may not be sent to a third-party device to compare against theplurality of reference text. In some examples, the plurality ofreference text can be generated on the electronic device based on userinput (e.g., ground truth donated by the user). In particular, prior toblock 906, the plurality of user speech samples can be transcribed(e.g., using the personalized speech recognition system or a remotespeech recognition system). The transcribed user speech samples can thenbe presented to the user on the electronic device for verification. Forexample, the user can be prompted to correct the transcribed user speechsamples if an error is noted. User input can then be received from theuser at the electronic device. In particular, the user input can correctthe transcribed user speech samples to generate the plurality ofreference text (e.g., verified text). The plurality of reference textcan thus be stored in the memory of the electronic device prior to block906 and then utilized to generate the plurality of accuracy scores.

In some examples, the plurality of reference text can be generated on aremote server and then uploaded onto the electronic device to generatethe plurality of accuracy scores. For example, prior to block 906, theplurality of user speech samples can be transmitted to a remote server(e.g., DA server 106 or distributed evaluation server 806). Theplurality of user speech samples can then be processed using a largevocabulary automatic speech recognition system of the remote server togenerate the plurality of reference text. The plurality of referencetext can then be uploaded and stored on the electronic device where itcan be used to generate the plurality of accuracy scores. The largevocabulary automatic speech recognition system can utilize one or morelarge speech recognition models and can implement system combination toproduce accurate results. For efficiency, in some examples, the resultsgenerated by the large vocabulary automatic speech recognition systemmay not be verified by an individual prior to being uploaded onto theelectronic device.

In other examples, the plurality of reference text can be verified by anindividual prior to being uploaded onto the electronic device. Inparticular, the plurality of reference text can be generated based onuser input received at the remote server. For example, the plurality ofuser speech samples can be transcribed at the remote server (e.g., usinga large vocabulary automatic speech recognition system as describedabove) and the transcribed user speech samples can be verified by anindividual to generate the plurality of reference text. Specifically,the individual can compare the user speech samples with the transcribeduser speech samples and provide user input to the remote server tocorrect portions of the transcribed user speech samples to generate theplurality of reference text. In other examples, the individual candirectly transcribe the plurality of user speech samples to generate theplurality of reference text. In particular, the individual can listen tothe plurality of user speech samples and manually generate the pluralityof reference text corresponding to the plurality of user speech samples.In these examples, user text input corresponding to a transcription ofthe plurality of user speech samples can be received at the remoteserver. The plurality of reference text can then be uploaded and storedon the electronic device where it can be compared against the pluralityof speech recognition results to generate the plurality of accuracyscores.

In some examples, the plurality of speech recognition results can becombined with a plurality of second speech recognition results togenerate a plurality of combined speech recognition results. Forexample, the plurality of user speech samples can be transmitted to aremote server (e.g., DA server 106) where a remote speech recognitionsystem on the remote server processes the plurality of user speechsamples to generate the plurality of second speech recognition results.The remote speech recognition system can be a general speech recognitionsystem that utilizes user-independent speech recognition models. Theplurality of second speech recognition results can be transmitted backto the electronic device where it can be combined with the plurality ofspeech recognition results. As described in greater detail below atblock 918, the combination can be performed in a streaming fashion whereeach second speech recognition result is streamed back to the electronicdevice as it is being generated and combined on-the-fly with therespective speech recognition result at a word level. Alternatively, thecombination can be performed as a separate step where each second speechrecognition result is sent back to the electronic device after it iscompletely generated and then combined with the respective speechrecognition result as a whole. The combination can be based on theinstructions received at block 902. For example, the combination canapply one or more interpolation weighting parameters defined in theinstructions of block 902. Additionally, the plurality of combinedspeech recognition results can be associated with a plurality ofcombined accuracy scores. In some examples, the plurality of combinedaccuracy scores can be based on the plurality of accuracy scoresassociated with the plurality of speech recognition results and aplurality of second accuracy scores associated with the plurality ofsecond speech recognition results. The plurality of combined accuracyscores can be used to evaluate the accuracy and performance of thecombined speech recognition system based on the one or moreinterpolation weighting parameters.

At block 908, the plurality of user speech samples can be processed(e.g., by client-side distributed evaluation module 817 and/or STTprocessing module 730) using the personalized speech recognition system.In particular, processing the plurality of user speech samples using thepersonalized speech recognition system can generate a plurality ofreference speech recognition results. Additionally, a plurality ofreference accuracy scores corresponding to the plurality of referencespeech recognition results can be generated by processing the pluralityof user speech samples using the personalized speech recognition system.The personalized speech recognition system can be the default speechrecognition system used by the electronic device. The plurality ofreference accuracy scores of block 908 can serve as a reference tocompare against the plurality of accuracy scores of block 906. In thisway, the relative performance of the second personalized speechrecognition system can be determined with respect to the personalizedspeech recognition system.

The plurality of reference accuracy scores can be generated in a similarmanner as the plurality of accuracy scores of block 906, discussedabove. In some examples, the plurality of reference accuracy scores canbe confidence scores, which represent estimated error rates (e.g.,estimated word error rates) rather than actual error rates. In theseexamples, the plurality of reference accuracy scores can be generatedwithout comparing the plurality of reference speech recognition resultsto a plurality of reference text. Additionally, the plurality ofreference accuracy scores can be derived from the same one or morespeech recognition models of the personalized speech recognition systemfrom which the plurality of accuracy scores of block 906 are derived.For example, if the plurality of accuracy scores at block 906 aregenerated using only the one or more acoustic models of the personalizedspeech recognition system, then the plurality of reference accuracyscores at block 908 are also generated using only the one or moreacoustic models of the personalized speech recognition system. Thisenables a meaningful comparison between the plurality of accuracy scoresof block 906 and the plurality of reference accuracy scores of block908.

In other examples, if the plurality of accuracy scores of block 906 aregenerated by comparing the plurality of speech recognition results to aplurality of reference text, then the plurality of reference accuracyscores can be generated in the same manner. Specifically, the pluralityof reference accuracy scores can be generated by comparing the pluralityof reference speech recognition results to a plurality of referencetext. The comparison can be performed at the electronic device. Asdiscussed above, the plurality of reference text can be generated at theelectronic device based on user input received at the electronic device.Alternatively, the plurality of reference text can be generated at theremote server using a remote speech recognition system and/or based onuser input received at the remote server.

At block 910, the plurality of accuracy scores of block 906 and/or theplurality of reference accuracy scores of block 908 can be transmitted(e.g., by client-side distributed evaluation module 817 and/or using RFcircuitry 208 or network communications interface 460, 708) to a remoteserver (e.g., distributed evaluation server 806) for evaluation. Forexample, the performance of the second personalized speech recognitionsystem (based on the one or more experimental parameters) can beevaluated by comparing the plurality of accuracy scores of block 906 tothe plurality of reference accuracy scores of block 908. The evaluationcan be performed automatically at the remote server (e.g., usingdistributed evaluation module 815). Alternatively, the evaluation can beperformed by an individual, such as the system developer (e.g., atalgorithm generating device 808).

In some examples, based on the plurality of accuracy scores and theplurality of reference accuracy scores, a determination can be made thatthe second personalized speech recognition system performs better thanthe personalized speech recognition system. In accordance with thisdetermination, the remote server can provide second instructions foractivating the second personalized speech recognition system.

It should be recognized that the evaluation of the one or moreexperimental parameters can be based on experiments performed on manyelectronic devices. For examples, blocks 902 to 910 can be performed ona population of devices (e.g., user devices 820-826). The population ofdevices can include a large number of devices (e.g., greater than 10,000devices) to evaluate the one or more experimental parameters at scale.Each device can receive instructions (block 902) defining the experimentfor evaluating the one or more experimental parameters. Each device canthen perform the experiment (blocks 904-908) to generate accuracy scoresand reference accuracy scores. The accuracy scores can correspond to asecond personalized speech recognition system generated based on the oneor more experimental parameters, and the reference accuracy scores cancorrespond to a default personalized speech recognition system of thedevice. Each device can then transmit the accuracy scores and thereference accuracy scores to the remote server (block 910) forevaluation. This enables the one or more experimental parameters to beevaluated at scale based on a large volume of data from the populationof devices. In particular, a determination can be made based on thelarge volume of data from the population devices whether the performanceof the second personalized speech recognition system is statisticallybetter than the performance of the personalized speech recognitionsystem. In response to determining that the second personalized speechrecognition system performs better than the personalized speechrecognition system, second instructions for activating the secondpersonalized speech recognition system can be provided based.

As discussed above, the accuracy scores and the reference accuracyscores can be confidence scores (e.g., estimated word error rates)rather than actual accuracy scores (e.g., actual word error rates). Inparticular, the accuracy scores and the reference accuracy scores can bebased on likelihood values obtained from one or more speech recognitionmodels used by the personalized speech recognition system and the secondpersonalized speech recognition system. Based on experimental data, ithas been confirmed that, at scale, confidence scores, such as estimatedword error rates, correlate relatively well to actual accuracy scores,such as actual word error rates. Thus, confidence scores can reliably beused to compare the accuracy of different speech recognition systems,such as the personalized speech recognition system compared to thesecond personalized speech recognition system. This is an unexpectedresult. Accordingly, as discussed above, process 900 for evaluatingembedded personalized systems can be performed using confidence scoreswithout having to determine actual accuracy scores.

In some examples, the instructions of block 902 can cause blocks 904 to910 to be performed in response to detecting one or more predeterminedoperating states of the user device. The one or more predeterminedoperating states can correspond to an idle state where performing theevaluation of the one or more experimental parameters would be leastdisruptive to the user. For example, the one or more predeterminedoperating states can include the user device being connected to a Wi-Finetwork, the user device being connect to a power source, and the timecorresponding to the evening period of between 12:00 am and 6:00 am.

At block 912, the second instructions for activating the secondpersonalized speech recognition system can be received (e.g., byclient-side distributed evaluation module 817 and via RF circuitry 208or network communications interface 460, 708). As discussed above, thesecond instructions can be based on the plurality of accuracy scores ofblock 906 and/or the plurality of reference accuracy scores of block908. In some examples, the second instructions can be based on aplurality of sets of accuracy scores obtained from a plurality of remoteelectronic devices. The plurality of sets of accuracy scores can begenerated at the plurality of remote electronic devices in accordancewith experimental instructions (e.g., instructions of block 902) thatdefine the one or more experimental parameters.

At block 914, the second personalized speech recognition system can beactivated (e.g., by client-side distributed evaluation module 817) inaccordance with the second instructions of block 912. The secondpersonalized speech recognition system can be activated in the STTprocessing module (e.g., STT process module 730) of the user device. Insome examples, activating the second personalized speech recognitionsystem can include modifying the personalized speech recognition systemby incorporating the one or more experimental parameters in thepersonalized speech recognition system. In these examples, subsequentlyreceived user speech input to be processed using the modifiedpersonalized speech recognition system that implements the one or moreexperimental parameters. In other examples, activating the secondpersonalized speech recognition system can include replacing thepersonalized speech recognition system with the second personalizedspeech recognition system. In these examples, subsequently received userspeech input can be processed using the second personalized speechrecognition system rather than the personalized speech recognitionsystem.

At block 916, user speech input can be received (e.g., at I/O processingmodule 728). The user speech input can be received after activating thesecond personalized speech recognition system at block 914. The userspeech input can be addressed to the digital assistant implemented onthe electronic device and can contain a user request in the form of anatural language command, request, statement, narrative, and/or inquiry.For example, as discussed above, the user speech input can includerequests such as, “Where am I right now?” or “Please invite my friendsto my girlfriend's birthday party next week.”

At block 918, the user speech input can be processed using the activatedsecond personalized speech recognition system to generate a speechrecognition result. The speech recognition result can be a textrepresentation (e.g., sequence of words or tokens) of the user speechinput. In some examples, block 918 can be performed using a STTprocessing module (STT processing module 730 of FIG. 7B). The functionsof the STT processing module can be divided between a remote server(e.g., DA server 106 of FIG. 1) and a client device (e.g., user device104 of FIG. 1). For example, the client device can include the secondpersonalized speech recognition system implementing a personalizedspeech recognition model and the remote server can include a remotespeech recognition system implementing a generic speech recognitionmodel. In these examples, the speech recognition result can be based ona combination of a first intermediate speech recognition resultgenerated by the second personalized speech recognition system of theclient device and second intermediate speech recognition resultgenerated by the remote speech recognition system of the remote server.

Upon receiving the user speech input of block 916, the user speech inputcan be processed using the second personalized speech recognition systemof the client device to generate the first intermediate speechrecognition result. At the same time, a copy of the user speech inputcan be sent to the remote server where it is processed by the remotespeech recognition system to generate the second intermediate speechrecognition result. In some examples, the second intermediate speechrecognition result can be sent back to the client device in a streamingfashion as it is being generated. This can occur while secondpersonalized speech recognition system of the client device isprocessing the user speech input to generate the first intermediatespeech recognition result. In these examples, the STT processing modulecan select between a streamed word of the second intermediate speechrecognition result and a corresponding word from the first intermediatespeech recognition result to incrementally build up the final combinedspeech recognition result.

In other examples, the second intermediate speech recognition result canbe sent back to the client device as a whole after it has been generatedby the remote speech recognition system. In these examples, the STTprocessing module can wait until both recognition systems are finishedand then generate the final combined speech recognition result byselecting words from both the first intermediate speech recognitionresult and the second intermediate speech recognition result.

Although in the above examples, the second intermediate speechrecognition result is sent back to the client device and combined withthe first intermediate speech recognition result on the client device togenerate the final combined speech recognition result, it should berecognized that in other examples, the first intermediate speechrecognition results can alternatively be sent to the remote server andcombined with the second intermediate speech recognition result on theremote server to generate the final combined speech recognition result.In some examples, the final combined speech recognition result can thenbe sent to the client device (e.g., for display on the client device).

At block 920, a response to the user speech input can be outputted basedon the speech recognition result. For example, speech recognitionresults for the user speech input “Where am I right now” can begenerated at block 918. The speech recognition results can be processedthrough a natural language processing module (e.g., natural languageprocess module 732) to determine the actionable intent of determiningthe current location of the user device. Tasks corresponding to theactionable intent can then be performed (e.g., using task flowprocessing module 736 and service processing module 738). The task caninclude, for example, retrieving the GPS coordinates corresponding tothe current location of the electronic device and displaying a map onthe electronic device indicating the current location. Further, the taskcan include providing the response (e.g., using dialogue processingmodule 734) such as “You are in Central Park near the west gate.”

Embedded statistical systems can often require trading off accuracy inorder to accommodate the lower memory capacities associated with userdevices. Specifically, embedded statistical systems can requireimplementing smaller, less accurate, personalized statistical models.For example, it was found that for user-specific speech input, a smallerscale, personalized language model that is suitable to be implemented onan embedded speech recognition system of a user device had a word errorrate of 11.3% (i.e., 11.3 errors per 100 words). In contrast, alarge-scale, personalized language model suitable to be implemented on aserver-based speech recognition system had a lower word error rate of10.9% (i.e., 10.9 errors per 100 words). However, as discussed above, inorder to preserve the privacy of the user, personalized language modelsare preferably implemented on embedded speech recognition systems of theuser device rather than on server-based speech recognition systems.Accordingly, server-based speech recognition systems would implementgeneric language models rather than personalized language models. Foruser-specific speech input, a large-scale, generic language modelsuitable to be implemented on a server-based speech recognition systemwas found to have a significantly higher word error rate of 11.8%.However, by using process 900 to optimally combine the results from aserver-based speech recognition system implementing the large-scalegeneric language model (11.8% word error rate) with the results from anembedded speech recognition system implementing the smaller scalepersonalized language model (11.3% word error rate), it was found that acombined word error rate of 11.0% can be achieved. This is anunexpectedly low error rate that is comparable to the 10.9% word errorrate achieved by the large-scale personalized language model. Thus,using process 900 to optimally combine results from a large-scale,generic server-based system with a small-scale but personalized embeddedsystem can produce unexpectedly low error rates that are comparable to alarge-scale, personalized server based system.

Although process 900 is described above with reference to a personalizedspeech recognition system implementing one or more speech recognitionmodels, it should be recognized that process 900 can be similarlyapplicable for evaluating other personalized statistical systems, suchas machine translation systems, natural language understanding system,or speech synthesis systems. For example, instead of “user speechsamples,” “personalized speech recognition system,” “speech recognitionresults,” and “second personalized speech recognition system,”referenced above, process 900 can be implemented using “user naturallanguage discourse samples,” “personalized natural language processingsystem,” “natural language results,” and “second natural languageprocessing system.” Similarly, based on the above described, one skilledin the art would recognize the necessary modifications required toimplement process 900 for evaluating other types of personalizedstatistical systems.

5. Other Electronic Devices

FIG. 10 shows a functional block diagram of electronic device 1000configured in accordance with the principles of the various describedexamples. The functional blocks of the device can be optionallyimplemented by hardware, software, or a combination of hardware andsoftware to carry out the principles of the various described examples.It is understood by persons of skill in the art that the functionalblocks described in FIG. 10 can be optionally combined or separated intosub-blocks to implement the principles of the various describedexamples. Therefore, the description herein optionally supports anypossible combination, separation, or further definition of thefunctional blocks described herein.

As shown in FIG. 10, electronic device 1000 can include touch screendisplay unit 1002 configured to display information and to receive inputfrom the user, audio input unit 1004 configured to receive audio input(e.g., speech input), speaker unit 1005 configured to output audio(e.g., speech), and communication unit 1006 configured to transmit andreceive information (e.g., instructions or speech samples). Electronicdevice 1000 can further include processing unit 1008 coupled to touchscreen display unit 1002, audio input unit 1004, speaker unit 1005, andcommunication unit 1006. In some examples, processing unit 1008 caninclude receiving unit 1010, generating unit 1012, speech processingunit 1014, activating unit 1016, outputting unit 1018, and transmittingunit 1020.

In accordance with some embodiments, processing unit 1008 is configuredto receive (e.g., with receiving unit 1010 and via communication unit1006) instructions that define an experiment associated with thepersonalized speech recognition system. The instructions define one ormore experimental parameters. Processing unit 1008 is further configuredto, in accordance with the received instructions, generate (e.g., withgenerating unit 1012) a second personalized speech recognition systembased on the personalized speech recognition system and the one or moreexperimental parameters. Processing unit 1008 is further configured to,in accordance with the received instructions, process (e.g., with speechprocessing unit 1014) the plurality of user speech samples using thesecond personalized speech recognition system to generate a plurality ofspeech recognition results and a plurality of accuracy scorescorresponding to the plurality of speech recognition results. Processingunit 1008 is further configured to receive (e.g., with receiving unit1010 and via communication unit 1006) second instructions based on theplurality of accuracy scores. Processing unit 1008 is further configuredto, in accordance with the second instructions, activate (e.g., withactivating unit 1016) the second speech recognition system. Processingunit 1008 is further configured to receive (e.g., with receiving unit1010 and via audio input unit 1004) user speech input. Processing unit1008 is further configured to process (e.g., with speech processing unit1014) the user speech input using the activated second personalizedspeech recognition system to generate a speech recognition result.Processing unit 1008 is further configured to output (e.g., withoutputting unit 1018 and via touch screen display unit 1002 or speakerunit 1005) a response to the user speech input based on the speechrecognition result.

In some examples, the one or more experimental parameters include one ormore machine learning hyperparameters.

In some examples, the one or more experimental parameters include one ormore weighting parameters for interpolating between a general speechrecognition model and a personalized speech recognition model of thepersonalized speech recognition system.

In some examples, the plurality of speech recognition results are nottransmitted to a remote electronic device.

In some examples, the plurality of accuracy scores are confidence scoresgenerated without comparing the plurality of speech recognition resultsto a plurality of reference text.

In some examples, the plurality of speech recognition results are basedon one or more speech recognition models of the personalized speechrecognition system and one or more second speech recognition modelsgenerated based on the one or more experimental parameters, and theplurality of accuracy scores are derived from the one or more speechrecognition models of the personalized speech recognition system.

In some examples, the plurality of accuracy scores are not derived fromthe one or more second speech recognition models.

In some examples, the memory stores a plurality of verified text. Theplurality of verified text are generated based on user input received atthe electronic device. The plurality of accuracy scores are generated bycomparing the plurality of speech recognition results to the pluralityof verified text.

In some examples, processing unit 1008 is further configured to transmit(e.g., with transmitting unit 1020) the plurality of user speech samplesto a remote electronic device. Processing unit 1008 is furtherconfigured to receive (e.g., with receiving unit 1010 and viacommunication unit 1006), from the remote electronic device, a pluralityof second verified text corresponding to the plurality of user speechsamples. The plurality of accuracy scores are generated by comparing theplurality of speech recognition results to the plurality of secondverified text.

In some examples, the plurality of second verified text is generated byprocessing the plurality of user speech samples using a large vocabularyautomatic speech recognition system of the remote electronic device.

In some examples, the plurality of second verified text is generatedbased on second user input received at the remote electronic device.

In some examples, processing unit 1008 is further configured to, priorto receiving the instructions, receive (e.g., with receiving unit 1010and via audio input unit 1004) a plurality of speech inputs at theelectronic device. The plurality of speech inputs are associated with auser. The user speech samples are derived from the plurality of speechinputs.

In some examples, the personalized speech recognition system includesone or more personalized speech recognition models trained using userdata.

In some examples, the second speech recognition system is inactive priorto receiving the second instructions.

In some examples, processing unit 1008 is further configured to process(e.g., with speech processing unit 1014) the plurality of user speechsamples using the personalized speech recognition system to generate aplurality of reference speech recognition results and a plurality ofreference accuracy scores corresponding to the plurality of referencespeech recognition results. The second instructions are based on theplurality of reference accuracy scores.

In some examples, the plurality of speech recognition results aregenerated by combining a plurality of second speech recognition resultsgenerated from the second speech recognition system with a plurality ofthird speech recognition results generated from a remote speechrecognition system.

In some examples, the second instructions are based on a plurality ofsets of accuracy scores obtained from a plurality of remote electronicdevices in accordance with experimental instructions defining the one ormore experimental parameters.

In some examples, the speech recognition result is generated based on aword-level combination of a second speech recognition result generatedfrom the second speech recognition system with a third speechrecognition result generated from a remote speech recognition system.

The operations described above with reference to FIGS. 9A-B can beoptionally implemented by components depicted in FIGS. 1-4, 6A-B, 7A,and 8. For example, the operations of process 900 may be implemented byone or more of operating system 718, applications module 724, I/Oprocessing module 728, I/O interface 818, STT processing module 730,User Data 748, distributed evaluation module 815, data & models 816,client distributed evaluation module 815, or processor(s) 220, 410, 704.It would be clear to a person having ordinary skill in the art how otherprocesses can be implemented based on the components depicted in FIGS.1-4, 6A-B, 7A, 8, and 10.

In accordance with some implementations, a computer-readable storagemedium (e.g., a non-transitory computer readable storage medium) isprovided, the computer-readable storage medium storing one or moreprograms for execution by one or more processors of an electronicdevice, the one or more programs including instructions for performingany of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises means forperforming any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises a processing unitconfigured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises one or moreprocessors and memory storing one or more programs for execution by theone or more processors, the one or more programs including instructionsfor performing any of the methods or processes described herein.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

As described above, one aspect of the present technology is thegathering and use of data available from various sources to improve thedelivery to users of invitational content or any other content that maybe of interest to them. The present disclosure contemplates that in someinstances, this gathered data may include personal information data thatuniquely identifies or can be used to contact or locate a specificperson. Such personal information data can include demographic data,location-based data, telephone numbers, email addresses, home addresses,or any other identifying information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used todeliver targeted content that is of greater interest to the user.Accordingly, use of such personal information data enables calculatedcontrol of the delivered content. Further, other uses for personalinformation data that benefit the user are also contemplated by thepresent disclosure.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. For example, personal informationfrom users should be collected for legitimate and reasonable uses of theentity and not shared or sold outside of those legitimate uses. Further,such collection should occur only after receiving the informed consentof the users. Additionally, such entities would take any needed stepsfor safeguarding and securing access to such personal information dataand ensuring that others with access to the personal information dataadhere to their privacy policies and procedures. Further, such entitiescan subject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof advertisement delivery services, the present technology can beconfigured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data duringregistration for services. In another example, users can select not toprovide location information for targeted content delivery services. Inyet another example, users can select to not provide precise locationinformation, but permit the transfer of location zone information.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, content can beselected and delivered to users by inferring preferences based onnon-personal information data or a bare minimum amount of personalinformation, such as the content being requested by the deviceassociated with a user, other non-personal information available to thecontent delivery services, or publically available information.

1. An electronic device for evaluating personalized embedded systems,the device comprising: one or more processors; and memory storing aplurality of user speech samples, a personalized speech recognitionsystem, and instructions, the instructions, when executed by the one ormore processors, cause the one or more processors to: receive secondinstructions that define an experiment associated with the personalizedspeech recognition system, wherein the second instructions define one ormore experimental parameters; in accordance with the received secondinstructions: generate a second personalized speech recognition systembased on the personalized speech recognition system and the one or moreexperimental parameters; and process the plurality of user speechsamples using the second personalized speech recognition system togenerate a plurality of speech recognition results and a plurality ofaccuracy scores corresponding to the plurality of speech recognitionresults; receive third instructions based on the plurality of accuracyscores; in accordance with the third instructions, activate the secondspeech recognition system; receive user speech input; process the userspeech input using the activated second personalized speech recognitionsystem to generate a speech recognition result; and output a response tothe user speech input based on the speech recognition result.
 2. Thedevice of claim 1, wherein the one or more experimental parametersinclude one or more machine learning hyperparameters.
 3. The device ofclaim 1, wherein the one or more experimental parameters include one ormore weighting parameters for interpolating between a general speechrecognition model and a personalized speech recognition model of thepersonalized speech recognition system.
 4. The device of claim 1,wherein the plurality of speech recognition results are not transmittedto a remote electronic device.
 5. The device of claim 1, wherein theplurality of accuracy scores are confidence scores generated withoutcomparing the plurality of speech recognition results to a plurality ofreference text.
 6. The device of claim 1, wherein: the memory stores aplurality of verified text, the plurality of verified text generatedbased on user input received at the electronic device; and the pluralityof accuracy scores are generated by comparing the plurality of speechrecognition results to the plurality of verified text.
 7. The device ofclaim 1, wherein the instructions further cause the one or moreprocessors to: transmit the plurality of user speech samples to a remoteelectronic device; and receive, from the remote electronic device, aplurality of second verified text corresponding to the plurality of userspeech samples, wherein the plurality of accuracy scores are generatedby comparing the plurality of speech recognition results to theplurality of second verified text.
 8. The device of claim 7, wherein theplurality of second verified text is generated by processing theplurality of user speech samples using a large vocabulary automaticspeech recognition system of the remote electronic device.
 9. The deviceof claim 7, wherein the plurality of second verified text is generatedbased on second user input received at the remote electronic device. 10.The device of claim 1, wherein the plurality of speech recognitionresults are based on one or more speech recognition models of thepersonalized speech recognition system and one or more second speechrecognition models generated based on the one or more experimentalparameters, and wherein the plurality of accuracy scores are derivedfrom the one or more speech recognition models of the personalizedspeech recognition system.
 11. The device of claim 10, wherein theplurality of accuracy scores are not derived from the one or more secondspeech recognition models.
 12. The device of claim 1, wherein theinstructions further cause the one or more processors to: prior toreceiving the second instructions, receive a plurality of speech inputsat the electronic device, wherein the plurality of speech inputs areassociated with a user, and wherein the user speech samples are derivedfrom the plurality of speech inputs.
 13. The device of claim 1, whereinthe second speech recognition system is inactive prior to receiving thethird instructions.
 14. The device of claim 1, wherein the instructionsfurther cause the one or more processors to: process the plurality ofuser speech samples using the personalized speech recognition system togenerate a plurality of reference speech recognition results and aplurality of reference accuracy scores corresponding to the plurality ofreference speech recognition results, wherein the third instructions arebased on the plurality of reference accuracy scores.
 15. The device ofclaim 1, wherein the plurality of speech recognition results aregenerated by combining a plurality of second speech recognition resultsgenerated from the second speech recognition system with a plurality ofthird speech recognition results generated from a remote speechrecognition system.
 16. The device of claim 1, wherein the thirdinstructions are based on a plurality of sets of accuracy scoresobtained from a plurality of remote electronic devices in accordancewith experimental instructions defining the one or more experimentalparameters.
 17. The device of claim 1, wherein the speech recognitionresult is generated based on a word-level combination of a second speechrecognition result generated from the second speech recognition systemwith a third speech recognition result generated from a remote speechrecognition system.
 18. A method for evaluating personalized embeddedsystems implemented on a device, comprising: at an electronic devicehaving one or more processors and memory storing a plurality of userspeech samples and a personalized speech recognition system: receivinginstructions that define an experiment associated with the personalizedspeech recognition system, wherein the instructions define one or moreexperimental parameters; in accordance with the received instructions:generating a second personalized speech recognition system based on thepersonalized speech recognition system and the one or more experimentalparameters; and processing the plurality of user speech samples usingthe second personalized speech recognition system to generate aplurality of speech recognition results and a plurality of accuracyscores corresponding to the plurality of speech recognition results;receiving second instructions based on the plurality of accuracy scores;in accordance with the second instructions, activating the second speechrecognition system; receiving user speech input; processing the userspeech input using the activated second personalized speech recognitionsystem to generate a speech recognition result; and outputting aresponse to the user speech input based on the speech recognitionresult.
 19. A non-transitory computer readable storage medium havinginstructions stored thereon, the instructions, when executed by one ormore processors, cause the one or more processors to: receive secondinstructions that define an experiment associated with a personalizedspeech recognition system, wherein the second instructions define one ormore experimental parameters; in accordance with the received secondinstructions: generate a second personalized speech recognition systembased on the personalized speech recognition system and the one or moreexperimental parameters; and process a plurality of user speech samplesusing the second personalized speech recognition system to generate aplurality of speech recognition results and a plurality of accuracyscores corresponding to the plurality of speech recognition results;receive third instructions based on the plurality of accuracy scores; inaccordance with the third instructions, activate the second speechrecognition system; receive user speech input; process the user speechinput using the activated second personalized speech recognition systemto generate a speech recognition result; and output a response to theuser speech input based on the speech recognition result.
 20. The methodof claim 18, wherein the one or more experimental parameters include oneor more machine learning hyperparameters.
 21. The method of claim 18,wherein the one or more experimental parameters include one or moreweighting parameters for interpolating between a general speechrecognition model and a personalized speech recognition model of thepersonalized speech recognition system.
 22. The method of claim 18,wherein the plurality of speech recognition results are not transmittedto a remote electronic device.
 23. The method of claim 18, wherein theplurality of accuracy scores are confidence scores generated withoutcomparing the plurality of speech recognition results to a plurality ofreference text.
 24. The method of claim 18, further comprising:transmitting the plurality of user speech samples to a remote electronicdevice; and receiving, from the remote electronic device, a plurality ofsecond verified text corresponding to the plurality of user speechsamples, wherein the plurality of accuracy scores are generated bycomparing the plurality of speech recognition results to the pluralityof second verified text.
 25. The computer readable storage medium ofclaim 19, wherein the one or more experimental parameters include one ormore machine learning hyperparameters.
 26. The computer readable storagemedium of claim 19, wherein the one or more experimental parametersinclude one or more weighting parameters for interpolating between ageneral speech recognition model and a personalized speech recognitionmodel of the personalized speech recognition system.
 27. The computerreadable storage medium of claim 19, wherein the plurality of speechrecognition results are not transmitted to a remote electronic device.28. The computer readable storage medium of claim 19, wherein theplurality of accuracy scores are confidence scores generated withoutcomparing the plurality of speech recognition results to a plurality ofreference text.
 29. The computer readable storage medium of claim 19,wherein the instructions further cause the one or more processors to:transmit the plurality of user speech samples to a remote electronicdevice; and receive, from the remote electronic device, a plurality ofsecond verified text corresponding to the plurality of user speechsamples, wherein the plurality of accuracy scores are generated bycomparing the plurality of speech recognition results to the pluralityof second verified text.